Mel Spectrum Analysis of Speech Recognition using Single Microphone

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1 International Journal of Engineering Research in Electronics and Communication Mel Spectrum Analysis of Speech Recognition using Single Microphone [1] Lakshmi S.A, [2] Cholavendan M [1] PG Scholar, Sree Sastha Institute of Engineering and Technology, Chennai. [2] Assistant Professor, Dept of Electronics and Communication Engineering. SSIET, Chennai. [1] [2] Abstract - Digital processing of speech signal is very important for high speed and precise automatic speech recognition technology. Speech recognition is the process in which certain words of a particular speaker will automatically recognized that are based on the information included in individual speech waves. Nowadays it is being used for health care, telephony military and people with disabilities therefore the digital signal processes such as feature extraction and feature matching are used for speech recognition. Inorder to extract the valuable information from speech signal, the data needs to be manipulated and analyzed. Basic method used for extracting the features of the speech signal is to find the Mel Frequency Cepstral Coefficients () and Mel Energy Dynamic Coefficients ().In this project, speech is recognised using single microphone based on the Mel spectrum analysis. This technique basically exploits the certain features of speech. and are used for extracting the features of speech and Dynamic Time Warping (DTW) is used for feature matching. The experimental results were analysed with the help of MATLAB and the results are simulated. Keywords: Mel frequency Cepstral Coefficients (), Mel Energy Dynamic Coefficients (), Dynamic Time Warping (DTW). I. INTRODUCTION Speech signal processing is one of the biggest developing areas of research in signal processing. The aim of digital speech signal processing is to take advantage of digital computing techniques to process the speech signal for increased understanding, improved communication and increased efficiency and productivity associated with speech activities. The major fields of speech signal processing consists of speech synthesis, speech recognition and speech/speaker transformation. Inorder to extract features from speech signal it has to be divided into fixed frames called windows. extraction approaches mostly depend upon modelling human voice production and modelling perception system. They provide a compressed representation of the speech signal by extracting a sequence of feature vectors. Speech recognition systems have a wide range of applications from the relatively simple isolated-word recognition systems for name-dialling, automated customer service and voice-control of cars and machines to continuous speech recognition as in auto-dictation or broadcast-news transcription. Potamitis described the robust estimation of speech presence probability of every spectral component of a speech signal impinging on a linear microphone array [9]. The performance of several objective measures was evaluated in terms of predicting the quality of noisy speech enhanced by noise suppression algorithms. [4]. Speech recognition is the process in which certain words of a particular speaker will automatically recognized that are based on the information included in individual speech waves. The proposed technique offers two phases namely training phase and testing phase. This project describes an approach of speech recognition by using and. Several features are extracted from speech signal of spoken words. and are extracted from speech signal of spoken words. DTW is used to cope with different speaking speeds in speech recognition. DTW is an algorithm, which is used for measuring similarity between two sequences, which may vary in time or speed. The experimental analysis is done with MATLAB and results are analysed. II. MEL SPECTRUM ANALYSIS Speech recognition system described in this paper comprises two phases namely training and testing. In training phase, the speech signal is acquired and presence and absence of speech signal is detected using Voice Activity Detector (VAD). The All Rights Reserved 2015 IJERECE 15

2 and features are then extracted and the data are stored as shown in Fig.1. Signal acquisition Fig. 1 Training Phase In testing phase, when a MATLAB program named speech test is executed it postulates to the user to choose any speech sample from the test group that are pre-recorded in the database. at the back end extracts the features of the chosen speech sample.here, the stored features are classified using DTW. DTW is an algorithm used for feature matching. The status of speech recognition is described. The block diagram for testing phase is shown in Fig. 2. Signal acquisition VAD VAD Classifier set Fig. 2 Testing Phase Store Store Recognition Status The s are frequently used as a speech parameterization in speech recognizers. Practical applications of speech recognition and dialogue systems bring sometimes a requirement to synthesize or reconstruct the speech from the saved or transmitted s. The Mel scale is a perceptual scale of pitches judged by listeners to be equal in distance from one another. The name Mel comes from the word melody to indicate that the scale is based on pitch comparisons. Thus for each tone with an actual frequency measured in Hz, a subjective pitch is measured on a scale called the Mel scale. is based on human hearing perceptions which cannot perceive frequencies over 1 KHz. In other words, in is based on known variation of the human ear s critical bandwidth with frequency. has two types of filter which are spaced linearly at low frequency below 1000 Hz and logarithmic spacing above 1000 Hz. A subjective pitch is present on Mel frequency scale to capture important characteristic of phonetic in speech. The VAD refers to a class of signal processing methods that detects if short segments of a speech signal contain voiced or unvoiced signal data. A VAD is normally using decision rules based on selected estimated signal features. VADs play a major role as a pre-processing block in a variety of speech processing applications such as speech enhancement, speech coding and speech recognition where it is desirable to classify voiced signal parts from unvoiced. The classifier uses the spectral feature set (feature vector) of the input (unknown) word and attempts to find the best match from the library of known words.dtw technique is used for feature matching. DTW is an efficient method to solve the time alignment problem. DTW algorithm aims at aligning two sequences of feature vectors by warping the time axis repetitively until an optimal match between the two sequences is found. This algorithm performs a piece wise linear mapping of the time axis to align both the signals. DTW first locally matches the features of the selected sampled speech signal by measuring the local distance. DTW then measures the global distance and the part that matches with the chosen sampled speech is the result of the test program that shows the correct spoken word in the command window. A. FEATURE EXTRACTION The extraction of the best parametric representation of acoustic signals is an important task to produce a better recognition performance. have been used for feature extraction which is mainly used for speech recognition system. The purpose for using is to enhance the effectiveness of in the field of speech processing. All Rights Reserved 2015 IJERECE 16

3 The Mel spectrum is used in recognition of information in conventional speech recognition system. The spectrum does not exploit the variations in fundamental resolution and hence is lower in accuracy to improve the accuracy of operation a spectral decomposition approach. consists of seven computational steps. Speech s Fig. 3 The feature extraction is shown in fig.3. Each step has its function and mathematical approaches as discussed briefly in the following: Pre-processing To enhance the accuracy and efficiency of the extraction processes, speech signals are normally pre-processed before features are extracted. The signal passed through a filter is processed which emphasizes higher frequencies. This process will increase the energy of signal at higher frequency. Framing Pre- Proces sing DCT The process of segmenting the speech samples obtained from Analog-to-Digital Conversion (ADC) into a small frame with the length within the range of 20 to 40 msec. The voice signal is divided into frames of N samples. Windowing Framing Log Windowing Mel Filter Ban FFT Hamming window is used as window shape by considering the next block in feature extraction processing chain and integrates all the closest frequency lines. The Hamming window equation is given as: If the window is defined as W (n), 0 n N-1 where W(n) = Hamming window N = number of samples in each frame Y(n) = Output signal X(n) = Input signal The result of windowing signal is shown below: Y n = X n. W n (1) W n = cos 2πn 0 n N 1 N 1 (2) Fast Fourier Transform To convert each frame of N samples from time domain into frequency domain. The Fourier transform is to convert the convolution of the glottal pulse and the vocal tract impulse response in the time domain. Mel Filter Bank Processing The frequencies range in FFT spectrum is very wide and voice signal does not follow the linear scale. Each filter s magnitude frequency response is triangular in shape and equal to unity at the centre frequency and decrease linearly to zero at centre frequency of two adjacent filters. Then, each filter output is the sum of its filtered spectral components. After that the equation (3) is used to compute the Mel for given frequency f in hertz. There two reason for using triangular filter bank are: Logarithm To smooth the magnitude spectrum and To reduce the size of the features involved The logarithm has the effect of changing multiplication into addition. It is a part of computation of Cepstrum. Therefore, this step simply converts the multiplication of the magnitude in the Fourier transform into addition. Discrete Cosine Transform This is the process to convert the log Mel spectrum into time domain using DCT. The result of the conversion is called. The set of coefficient is called acoustic vectors. Therefore, each input utterance is transformed into a sequence of acoustic vector. DCT is applied on the log energy obtained from the triangular filter banks to get Mel scale cepstral coefficients. B. FEATURE EXTRACTION Another feature is also extracted. It is extracted as follows: the magnitude spectrum of each speech utterance is estimated using FFT, then input to a bank of 12 filters equally spaced on the All Rights Reserved 2015 IJERECE 17

4 Mel frequency scale. The logarithm mean energies of the filter outputs are calculated En(i), i= 1...N. Then, the first and second differences of En (i) are calculated. The feature extraction process contains following steps shown in fig. 5. Speech Pre- Processing Framing Windowing FFT Fig. 6 Test speech signal VAD detects the voiced or unvoiced signal data in the short segment of speech signal. VAD is based on zero crossing rate. Zero crossing rate can be defined as the number of times the successive samples in a speech signal have different algebraic signs or the amplitude of signal crosses the value of zero. The voice activity detected speech signal is shown in fig.7. s Compute 1 st and 2 nd Energies Mean Log Energie s Mel Filter Bank Fig.5 Pre-processing, framing, windowing, FFT and Mel filter bank and frequency wrapping processes of feature extraction are same as feature extraction. Take logarithmic mean of energies: In this process a mean log of every filter energies is calculated. This mean value represent energy of individual filter in a filter bank. Fig.7 VAD Speech Signal Compute 1 st and 2 nd difference: The final Mel energy spectrum dynamics coefficients are then obtained by combining the first and second differences of filter energies. I. EXPERIMENTAL RESULTS A. FEATURE EXTRACTION Fig. 8 Framed Speech Signal.Speech features such as and are extracted from speech. Speech signal is converted to.wav format and simulated using MATLAB. The test speech signal is shown in fig. 6. Fig. 9 FFT Log Mel spectrum is converted back to time. DCT can be used for calculating Coefficients from All Rights Reserved 2015 IJERECE 18

5 the given log Mel spectrum as they divide a given sequence of finite length data into discrete vector. The result is called the. The DCT is done for transforming the Mel coefficients back to time domain. The DCT applied signal is shown in fig. 11. A mean log of every filter energies is calculated. This mean value represent energy of individual filter in a filter bank. After taking logarithm, mean log energies are evaluated as shown in fig. 13. Fig. 10 Logarithm DCT is used to orthogonalise the filter energy vectors. The information is compacted into the first number of components and shortens the vector to number of components. Fig.13 Mean log energies Fig. 11 DCT B. FEATURE EXTRACTION The speech signal is divided into sequence of frames, where each frame can be analyzed independently and represented by a single feature vector. Framed speech signal for feature extraction is shown in fig.12. Fig.14 - First mean difference Fig. 15 Second Mean difference Fig Framed Speech Signal The final Mel energy spectrum dynamics coefficients are then obtained by combining the first and second differences of filter CONCLUSION All Rights Reserved 2015 IJERECE 19

6 The main aim of this project was to recognize speech using Mel spectrum analysis. The feature extraction was done using and. The feature matching was done with the help of DTW technique. The extracted features were stored in a.mat file using algorithm. A distortion measure based on minimizing the Euclidean distance was used when matching the unknown speech signal with the speech signal database. The experimental results were analysed with the help of MATLAB and it is proved that the results are efficient. This process can be extended for n number of speakers. The project shows that the Mel spectrum is the best nonlinear feature extracting technique in speech recognition, with minimal error rates and fast computing speed. This project may be extended by hardware implementation using digital signal processor. REFERENCES [1] Chang J. H., Jo Q. H., Kim D. K. and Kim N. K (2009) Global soft decision employing support vector machine for speech enhancement, IEEE Signal Processing, Lett., Vol. 6, No. 1, pp [2] Chang J. H., Kim N. S. and Mitra.S. K (2006) Voice activity detection based on multiple statistical models, IEEE Trans. Signal Process., Vol. 54, No. 6, pp [3] Cohen I. and Berdugo B. (2002) Noise estimation by minima controlled recursive averaging for robust speech enhancement, IEEE Signal process. Lett., Vol. 9, No. 1, pp [4] Hu Y. and Loizou P. (2008) Evaluation of objective quality measures of speech enhancement, IEEE Trans.Audio, Speech and Language Processing, Vol. 16, No. 1, pp [5] Krishnamoorthy N. and Hansen J. H. L. (2009) Babble noise : Modeling, analysis and applications, IEEE Audio, Speech and Language Processing, Vol. 17, No. 7, pp [6] Kum J. M. and Chang J. H. (2009) Speech enhancement based on minima controlled recursive averaging incorporating secondorder conditional MAP criterion, IEEE Signal Processing, Lett., Vol. 16, No. 7, pp [7] Martin R. (2001) Noise Power Spectral Density Estimation based on Optimal Smoothing and Minimum Statistics, IEEE transactions on speech and audio process., Vol. 9, No. 5, pp [8] Pirinen T. W. and Visa A. (2006) Signal independent wideband activity detection features for microphone arrays, in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., pp [9] Potamitis I. (2004) Estimation of speech presence probability in the field of microphone array, IEEE Signal Process. Lett., Vol. 11, No. 12, pp [10] Sohn J., Kim N. S. and Sung W. (1999) A Statistical model based voice activity detection, IEEE Signal Process. Lett., Vol. 6, No. 1, pp. 1. All Rights Reserved 2015 IJERECE 20

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